Extracting Rules from Incomplete Decision Systems: System ERID

Abstract

We present a new method for extracting rules from incomplete Information Systems (IS) whichare generalizations of information systems introduced by Pawlak [7]. Namely, we allow to use a set of weighted attribute values instead of a single value to describe objects in IS. The proposed strategy has some similarities with system LERS [3]. It is a bottom-up strategy, guided by two thresholds values (minimum support and minimum confidence) and generating sets of weighted objects with descriptions of minimal length. The algorithm starts with identifying sets of objects havingdescriptions of length one (values of attributes). Some of these sets satisfy both thresholds values and they are used for constructing rules. They are marked as successful. All sets having a number of supporting objects below the threshold value are marked as unsuccessful. Pairs of descriptions of all remaining sets (unmarked) are used to construct new sets of weighted objects having descriptions of length 2. This process is continued recursively by moving to sets of weighted objects having k-value properties. In [10], [1], ERID is used as a null value imputation toll for knowledge discovery based chase algorithm.